Using DCE and ranking data to estimate cardinal values for health states for deriving a preference-based single index from the sexual quality of life questionnaire.
ABSTRACT There is an increasing interest in using data derived from ordinal methods, particularly data derived from discrete choice experiments (DCEs), to estimate the cardinal values for health states to calculate quality adjusted life years (QALYs). Ordinal measurement strategies such as DCE may have considerable practical advantages over more conventional cardinal measurement techniques, e.g. time trade-off (TTO), because they may not require such a high degree of abstract reasoning. However, there are a number of challenges to deriving the cardinal values for health states using ordinal data, including anchoring the values on the full health-dead scale used to calculate QALYs. This paper reports on a study that deals with these problems in the context of using two ordinal techniques, DCE and ranking, to derive the cardinal values for health states derived from a condition-specific sexual health measure. The results were compared with values generated using a commonly used cardinal valuation technique, the TTO. This study raises some important issues about the use of ordinal data to produce cardinal health state valuations.
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ABSTRACT: This paper examines the distribution of preferences among respondents to a discrete choice experiment on the choice of general practitioner appointments. In addition to standard logit, mixed and latent class logit models are used to analyse the data from the choice experiment. It is found that there is significant preference heterogeneity for all the attributes in the experiment and that both the mixed and latent class models lead to significant improvements in fit compared to the standard logit model. Moreover, the distribution of preferences implied by the preferred mixed and latent class models is similar for many attributes.Journal of Health Economics 08/2008; 27(4):1078-94. · 1.60 Impact Factor
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ABSTRACT: Experimental design is critical to valid inference from the results of discrete choice experiments (DCEs). In health economics, DCEs have placed limited emphasis on experimental design, typically employing relatively small fractional factorial designs, which allow only strictly linear additive utility functions to be estimated. The extensive literature on optimal experimental design outside health economics has proposed potentially desirable design properties, such as orthogonality, utility balance and level balance. However, there are trade-offs between these properties and emphasis on some properties may increase the random variability in responses, potentially biasing parameter estimates.This study investigates empirically the design properties of DCEs, in particular, the optimal method of combining alternatives in the choice set. The study involves a forced choice between two alternatives (treatment and non-treatment for a hypothetical health care condition), each with three, four-level, alternative-specific attributes. Three experimental design approaches are investigated: a standard six-attribute, orthogonal main effects design; a design that combines alternatives to achieve utility balance, ensuring no alternatives are dominated; and a design that combines alternatives randomly. The different experimental designs did not impact on the underlying parameter estimates, but imposing utility balance increases the random variability of responses.Health Economics 05/2005; 14(4):349-62. · 2.23 Impact Factor
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ABSTRACT: Quality of life measures are increasingly important in evaluating outcomes in asthma. If some asthma symptoms are more troublesome to patients than others, this may affect their contribution to outcome measures. This study was designed to assess the relative importance of common symptoms in adults with asthma. A postal survey using conjoint analysis was performed in 272 adults attending hospital outpatient clinics with moderately severe asthma. Patients were asked to chose between "symptom scenarios" offering different combinations of levels of five common asthma symptoms over one week. Two versions of the questionnaire were used with identical scenarios presenting symptoms in different orders. Different patients answered the two versions. Regression analysis was used to calculate symptom weights for daytime cough, breathlessness, wheeze and chest tightness, and sleep disturbance. Symptom order, percentage predicted peak expiratory flow (PEF), and symptoms in the week before the survey did not influence the choice of scenario. In both questionnaires patients were more likely to choose scenarios with low levels of cough and breathlessness than low sleep disturbance, wheeze or chest tightness. Regression weights for cough (-0.52) and breathlessness (-0.49) were twice those of wheeze (-0.25), chest tightness (-0.27), and sleep disturbance (-0.25). For 12% of patients cough dominated patient preferences, regardless of all other symptoms. Age was inversely related to weight given by patients to breathlessness. The prominence of cough among other asthma symptoms was unexpected. Daytime cough and breathlessness had greater impact for patients than wheeze or sleep disturbance. Age influenced symptom burden, with younger patients giving greater weight to breathlessness than older patients. Conjoint analysis appears to be a useful method for establishing the relative importance of common symptoms.Thorax 03/2001; 56(2):138-42. · 8.38 Impact Factor